1 code implementation • EACL (GWC) 2021 • Oscar Sainz, German Rigau
In this paper we present a system that exploits different pre-trained Language Models for assigning domain labels to WordNet synsets without any kind of supervision.
1 code implementation • EMNLP 2021 • Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka, Ander Barrena, Eneko Agirre
In our experiments on TACRED we attain 63% F1 zero-shot, 69% with 16 examples per relation (17% points better than the best supervised system on the same conditions), and only 4 points short to the state-of-the-art (which uses 20 times more training data).
1 code implementation • 27 Oct 2023 • Oscar Sainz, Jon Ander Campos, Iker García-Ferrero, Julen Etxaniz, Oier Lopez de Lacalle, Eneko Agirre
In this position paper, we argue that the classical evaluation on Natural Language Processing (NLP) tasks using annotated benchmarks is in trouble.
1 code implementation • 5 Oct 2023 • Oscar Sainz, Iker García-Ferrero, Rodrigo Agerri, Oier Lopez de Lacalle, German Rigau, Eneko Agirre
In this paper, we propose GoLLIE (Guideline-following Large Language Model for IE), a model able to improve zero-shot results on unseen IE tasks by virtue of being fine-tuned to comply with annotation guidelines.
Ranked #1 on Zero-shot Named Entity Recognition (NER) on HarveyNER (using extra training data)
1 code implementation • 20 Apr 2023 • Iker García-Ferrero, Jon Ander Campos, Oscar Sainz, Ander Salaberria, Dan Roth
Named Entity Recognition (NER) is a core natural language processing task in which pre-trained language models have shown remarkable performance.
Multilingual Named Entity Recognition named-entity-recognition +4
no code implementations • 7 Feb 2023 • Oscar Sainz, Oier Lopez de Lacalle, Eneko Agirre, German Rigau
Language Models are the core for almost any Natural Language Processing system nowadays.
1 code implementation • Findings (NAACL) 2022 • Oscar Sainz, Itziar Gonzalez-Dios, Oier Lopez de Lacalle, Bonan Min, Eneko Agirre
In this work we show that entailment is also effective in Event Argument Extraction (EAE), reducing the need of manual annotation to 50% and 20% in ACE and WikiEvents respectively, while achieving the same performance as with full training.
Ranked #1 on Event Argument Extraction on WikiEvents
2 code implementations • NAACL (ACL) 2022 • Oscar Sainz, Haoling Qiu, Oier Lopez de Lacalle, Eneko Agirre, Bonan Min
The current workflow for Information Extraction (IE) analysts involves the definition of the entities/relations of interest and a training corpus with annotated examples.
no code implementations • 1 Nov 2021 • Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heinz, Dan Roth
Large, pre-trained transformer-based language models such as BERT have drastically changed the Natural Language Processing (NLP) field.
1 code implementation • 8 Sep 2021 • Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka, Ander Barrena, Eneko Agirre
In our experiments on TACRED we attain 63% F1 zero-shot, 69% with 16 examples per relation (17% points better than the best supervised system on the same conditions), and only 4 points short to the state-of-the-art (which uses 20 times more training data).
Ranked #10 on Relation Extraction on TACRED
1 code implementation • 7 Jan 2021 • Oscar Sainz, German Rigau
In this paper we present a system that exploits different pre-trained Language Models for assigning domain labels to WordNet synsets without any kind of supervision.
Ranked #1 on Domain Labelling on BabelDomains
no code implementations • LREC 2020 • Oscar Sainz, Oier Lopez de Lacalle, Itziar Aldabe, Montse Maritxalar
In this paper we present a relation extraction system that given a text extracts pedagogically motivated relation types, as a previous step to obtaining a semantic representation of the text which will make possible to automatically generate questions for reading comprehension.